UC Berkeley & Intel’s Photorealistic Denoising Method Boosts Video Quality on Moonless Nights

In the new paper Dancing Under the Stars: Video Denoising in Starlight, a research team from UC Berkeley and Intel Labs leverages a GAN-tuned, physics-based noise model to represent camera noise under low light conditions and trains a novel denoiser that, for the first time, achieves photorealistic video denoising in starlight.

ai artificial intelligence computer vision & graphics deep-neural-networks denoising intel machine learning machine learning & data science ml quality research technology uc berkeley video video denoising